
import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="""%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s""",
    datefmt="""%Y-%m-%d %H:%M:%S""",
    level=os.environ.get("""LOGLEVEL""", """INFO""").upper(),
    stream=sys.stdout,
)

lowerCamelCase_    :			Dict    =	logging.getLogger(__name__)

lowerCamelCase_    :			Union[str, Any]    =	{"""facebook/bart-base""": BartForConditionalGeneration}
lowerCamelCase_    :			Tuple    =	{"""facebook/bart-base""": BartTokenizer}







def 							A__       (    )	->						Optional[int]:
				UpperCamelCase_:				Optional[int]						=    argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""    )
				parser.add_argument(
				    """--validation_file"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""A csv or a json file containing the validation data."""    )
				parser.add_argument(
				    """--max_length"""  ,     type=lowerCamelCase  ,     default=5  ,     help="""The maximum total input sequence length after tokenization."""  ,     )
				parser.add_argument(
				    """--num_beams"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help=(
				        """Number of beams to use for evaluation. This argument will be """
				        """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."""
				    )  ,     )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--config_name"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""Pretrained config name or path if not the same as model_name"""  ,     )
				parser.add_argument(
				    """--device"""  ,     type=lowerCamelCase  ,     default="""cpu"""  ,     help="""Device where the model will be run"""  ,     )
				parser.add_argument("""--output_file_path"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""Where to store the final ONNX file."""    )

				UpperCamelCase_:				Union[str, Any]						=    parser.parse_args()

				return args







def 							A__       (    lowerCamelCase  ,     lowerCamelCase="cpu"    )	->						Dict:
				UpperCamelCase_:				Dict						=    model_dict[model_name].from_pretrained(lowerCamelCase    ).to(lowerCamelCase    )
				UpperCamelCase_:				Optional[int]						=    tokenizer_dict[model_name].from_pretrained(lowerCamelCase    )

				if model_name in ["facebook/bart-base"]:
								UpperCamelCase_:				Union[str, Any]						=    0
								UpperCamelCase_:				List[Any]						=    None
								UpperCamelCase_:				Dict						=    0

				return huggingface_model, tokenizer







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						str:
				model.eval()

				UpperCamelCase_:				Dict						=    None
				UpperCamelCase_:				List[str]						=    torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase    )    )

				with torch.no_grad():
								UpperCamelCase_:				Optional[int]						=    """My friends are cool but they eat too many carbs."""
								UpperCamelCase_:				Optional[Any]						=    tokenizer([ARTICLE_TO_SUMMARIZE]  ,     max_length=10_24  ,     return_tensors="""pt"""    ).to(model.device    )

								UpperCamelCase_:				Union[str, Any]						=    model.generate(
								    inputs["""input_ids"""]  ,     attention_mask=inputs["""attention_mask"""]  ,     num_beams=lowerCamelCase  ,     max_length=lowerCamelCase  ,     early_stopping=lowerCamelCase  ,     decoder_start_token_id=model.config.decoder_start_token_id  ,     )

								torch.onnx.export(
								    lowerCamelCase  ,     (
								        inputs["""input_ids"""],
								        inputs["""attention_mask"""],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    )  ,     lowerCamelCase  ,     opset_version=14  ,     input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""]  ,     output_names=["""output_ids"""]  ,     dynamic_axes={
								        """input_ids""": {0: """batch""", 1: """seq"""},
								        """output_ids""": {0: """batch""", 1: """seq_out"""},
								    }  ,     example_outputs=lowerCamelCase  ,     )

								logger.info("""Model exported to {}""".format(lowerCamelCase    )    )

								UpperCamelCase_:				Optional[int]						=    remove_dup_initializers(os.path.abspath(lowerCamelCase    )    )

								logger.info("""Deduplicated and optimized model written to {}""".format(lowerCamelCase    )    )

								UpperCamelCase_:				Dict						=    onnxruntime.InferenceSession(lowerCamelCase    )
								UpperCamelCase_:				Dict						=    ort_sess.run(
								    lowerCamelCase  ,     {
								        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
								        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
								        """num_beams""": np.array(lowerCamelCase    ),
								        """max_length""": np.array(lowerCamelCase    ),
								        """decoder_start_token_id""": np.array(model.config.decoder_start_token_id    ),
								    }  ,     )

								np.testing.assert_allclose(summary_ids.cpu().numpy()  ,     ort_out[0]  ,     rtol=1E-3  ,     atol=1E-3    )

								logger.info("""Model outputs from torch and ONNX Runtime are similar."""    )
								logger.info("""Success."""    )







def 							A__       (    )	->						List[str]:
				UpperCamelCase_:				Any						=    parse_args()
				UpperCamelCase_:				Optional[int]						=    5
				UpperCamelCase_:				str						=    4

				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,     datefmt="""%m/%d/%Y %H:%M:%S"""  ,     level=logging.INFO  ,     )

				logger.setLevel(logging.INFO    )
				transformers.utils.logging.set_verbosity_error()

				UpperCamelCase_:				Optional[Any]						=    torch.device(args.device    )

				UpperCamelCase_, UpperCamelCase_:				Any						=    load_model_tokenizer(args.model_name_or_path  ,     lowerCamelCase    )

				if model.config.decoder_start_token_id is None:
								raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined"""    )

				model.to(lowerCamelCase    )

				if args.max_length:
								UpperCamelCase_:				Optional[int]						=    args.max_length

				if args.num_beams:
								UpperCamelCase_:				Tuple						=    args.num_beams

				if args.output_file_path:
								UpperCamelCase_:				str						=    args.output_file_path
				else:
								UpperCamelCase_:				List[Any]						=    """BART.onnx"""

				logger.info("""Exporting model to ONNX"""    )
				export_and_validate_model(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()








from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[Any]						=    len(lowerCamelCase    ) // 2

				# choose the middle 3 elements
				UpperCamelCase_:				Tuple						=    lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]    ) == 2:
												m -= 1
								return peak(lst[m:]    )

				# decreasing
				else:
								if len(lst[:m]    ) == 2:
												m += 1
								return peak(lst[:m]    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






